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Abstract

The most interaction between humankind and water occurs in coastal and inland waters (Case 2 waters) at a scale of tens or hundred of meters, but there is not yet an ocean color product (e.g. chlorophyll-a product) at this spatial scale. Landsat 8 could potentially address the remote sensing of these kinds of waters due to its improved features. This work presents an approach to obtain the color producing agents (CPAs) chlorophyll-a, colored dissolved organic material (CDOM) and suspended material (SM) from water bodies using Landsat 8. Adequate atmospheric correction becomes an important first step to accurately retrieving water parameters since the sensor-reaching signal due to water is very small when compared to the signal due to the atmospheric effects. We developed the model-based empirical line method (MoB-ELM) atmospheric correction method. The Mob-ELM employs pseudo invariant feature (PIF) pixels extracted from a reflectance product along with the in-water radiative transfer model HydroLight. We used a look-up-table-based (LUT-based) inversion methodology to simultaneously retrieve CPAs. The LUT of remote-sensing reflectance spectra was created in Hydrolight using inherent optical properties (IOPs) measured in the field.

The retrieval algorithm was applied over three Landsat 8 scenes. The CPA concentration maps exhibit expected trends of low concentrations in clear waters and higher concentrations in turbid waters. We estimated a normalized root mean squared error (NRMSE) of about 14% for Chlorophyll-a, 11% for the total suspended solid (TSS), and 7% for colored dissolved organic matter (CDOM) when compared with in situ data. These results demonstrate that the developed algorithm allows the simultaneous mapping of concentration of all CPAs in Case 2 waters and over areas where the standard algorithms are not available due to spatial resolution. Therefore, this study shows that the Landsat 8 satellite can be utilized over Case 2 waters as long as a careful atmospheric correction is applied and IOPs are known.

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